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HT-29 has an epithelial appearance as a human colorectal cancer cell line. Early detection of colorectal cancer can enhance survival rates. This study aims to detect and count HT-29 cells using a deep-learning approach (ResNet-50). The cell lines were procured from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Further, the dataset is self-prepared in lab experiments, cell culture, and collected 566 images. These images contain two classes; the HT-29 human colorectal adenocarcinoma cells (blue shapes in bunches) and impurities (tinny circular grey shapes). These images are annotated with the help of an image labeller as impurity and cancer cells. Then afterwards, the images are trained, validated, and tested against the deep learning approach ResNet50. Finally, in each image, the number of impurity and cancer cells are counted to find the accuracy of the proposed model. Accuracy and computational expense are used to gauge the network’s performance. Each model is tested ten times with a non-overlapping train and random test splits. The effect of data pre-processing is also examined and shown in several tasks. The results show an accuracy of 95.5% during training and 95.3% in validation for detecting and counting HT-29 cells. HT-29 cell detection and counting using deep learning is novel due to the scarcity of research in this area, the application of deep learning, and potential performance improvements over traditional methods. By addressing a gap in the literature, employing a unique dataset, and using custom model architecture, this approach contributes to advancing colon cancer understanding and diagnosis techniques.

The current study aimed to assess the profiles of plasma amino acids, serum ammonia and oxidative stress status among autistic children in terms of electroencephalogram findings and clinical severity among the cohort of autistic Egyptian children. The present study included 118 Egyptian children categorized into 54 children with autism who were comparable with 64 healthy controls. Clinical assessments of cases were performed using CARS in addition to EEG records. Plasma amino acids were measured using high-performance liquid chromatography (HPLC), while, serum ammonia and oxidative stress markers were measured using colorimetric methods for all included children. The overall results revealed that 37.04% of cases had abnormal EEG findings. Amino acid profile in autistic children showed statistically significant lower levels of aspartic acid, glycine, β-alanine, tryptophan, lysine and proline amino acids with significantly higher asparagine amino acid derivative levels among autistic patients versus the control group (p˂0.05). There were significantly higher serum ammonia levels with significantly higher total oxidant status (TOS) and oxidative stress index (OSI) values among the included autistic children vs controls (p˂0.05). There were significantly negative correlations between CARS with aspartic acid (r=-0.269, P=0.049), arginine (r= - 0.286, p= 0.036), and TAS (r= -0.341, p= 0.012), and significantly positive correlations between CARS with TOS (r=0.360, p= 0.007) and OSI (r= 0.338, p= 0.013). Dysregulated amino acid metabolism, high ammonia and oxidative stress were prevalent among autistic children and should be considered in autism management. Still EEG records were inconclusive among autistic children, although may be helpful in assessment autism severity.

Saleem TH, Shehata GA, Toghan R, et al. Neuropsychiatr Dis Treat. 2020;16:11-24. The authors advised that the funding section was incorrectly presented in the original article and was missed during the revision process.Read the original article here